João Borges de Sousa

Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless

Unmanned aerial vehicles (UAVs) are mainly used by military and government organizations, but with low-cost sensors, electronics, and airframes there is significant interest in using low-cost UAVs among aircraft hobbyists, academic researchers, and industries. Applications such as mapping, search and rescue, patrol, and surveillance require the UAV to autonomously follow a predefined path at a prescribed height. The most commonly used paths are straight lines and circular orbits. Path-following algorithms ensure that the UAV will follow a predefined path in three or two dimensions at constant height. A basic requirement for these path-following algorithms is that they must be accurate and robust to wind disturbances.

IMC: A communication protocol for networked vehicles and sensors

This paper presents the Inter-Module Communication (IMC) protocol, a message-oriented protocol designed and implemented in the Underwater Systems and Technology Laboratory (LSTS) to build interconnected systems of vehicles, sensors and human operators that are able to pursue common goals cooperatively by exchanging real-time information about the environment and updated objectives. IMC abstracts hardware and communication heterogeniety by providing a shared set of messages that can be serialized and transferred over different means.

AUV Control and Communication using Underwater Acoustic Networks

Underwater acoustic networks can be quite effective to establish communication links between autonomous underwater vehicles (AUVs) and other vehicles or control units, enabling complex vehicle applications and control scenarios. A communications and control framework to support the use of underwater acoustic networks and sample application scenarios are described for single and multi-AUV operation.

Optimization of Wireless Sensor Network and UAV Data Acquisition

This paper deals with selection of sensor network communication topology and the use of Unmanned Aerial Vehicles (UAVs) for data gathering. The topology consists of a set of cluster heads that communicate with the UAV. In conventional wireless sensor networks Low Energy Adaptive Clustering Hierarchy (LEACH) is commonly used to select cluster heads in order to conserve energy. Energy conservation is far more challenging for large scale deployments. Particle Swarm Optimization (PSO) is proposed as an optimization method to find the optimal topology in order to reduce the energy consumption, Bit Error Rate (BER), and UAV travel time. PSO is compared to LEACH using a simulation case and the results show that PSO outperforms LEACH in terms of energy consumption and BER, while the UAV travel time is similar. The numerical results further illustrate that the performance gap between them increases with the number of cluster head nodes. Because of reduced energy consumption, network life time can be significantly extended while increasing the amount of data received from the entire network. By considering the wind effect into the PSO scheme, it is shown that this has an impact on the traveling time for the UAV but BER and energy consumption are not significantly increased.

Performance evaluation of cooperative relay and Particle Swarm Optimization path planning for UAV and wireless sensor network

Energy efficiency is crucial when wireless nodes are deployed in a remote or isolated area. This paper provides an optimal solution for data gathering from a wide area Wireless Sensor Network (WSN) with the use of Unmanned Aerial Vehicles (UAVs). Particle Swarm Optimization (PSO) is proposed as an optimization method to find the waypoints for a UAV in order to reduce the energy consumption and bit error rate (BER) of the sensor nodes, and the UAV travel time. In our previous work, the sensor nodes were required to transmit data to a Cluster Head (CH) node, which then forwarded the data to the UAV. The waypoints were restricted to be straight above the CH. In this work we employ cooperative relay, to make the data gathering more efficient. In addition, the waypoints of the UAV can be selected freely. To illustrate the effectiveness, we compare our new strategy to the one of our previous paper. Numerical results illustrate that the performance gap between them increases with the number of waypoints, in favor of the new strategy. Furthermore, the optimal number of waypoints for the UAV is also described, based on the size of the sensor network area and the density of the sensor node distribution. These contributions have maximized the network lifetime and communication quality, while minimized the UAVs flying time.

Bridging Cooperative Sensing and Route Planning of Autonomous Vehicles

Autonomous Vehicles (AV) are used to solve the problem of data gathering in large scale sensor deployments with disconnected clusters of sensors networks. Our take is that an efficient strategy for data collection with AVs should leverage i) cooperation amongst sensors in communication range of each other forming a sensor cluster, ii) advanced coding and data storage techniques for easing the cooperation process, and iii) AV route-planning that is both content and cooperation-aware. Our work formulates the problem of efficient data gathering as a cooperative route-optimization problem with communication constraints. We also analyze (network) coded data transmission and storage for simplifying cooperation amongst sensors as well as data collection by the AV. Given the complexity of the problem, we focus on heuristic techniques, such as particle swarm optimization, to calculate the AVs route and the times for communication with each sensor and/or cluster of sensors. We analyze two extreme cases, i.e., networks with and without intra- cluster cooperation, and provide numerical results to illustrate that the performance gap between them increases with the number of nodes. We show that cooperation in a 100 sensor deployment can increase the amount of data collected by up to a factor of 3 with respect to path planning without cooperation.

Multiple UAV area decomposition and coverage

Multiple UAVs can be used to cover a region effectively. Area coverage involves two stages - area decomposition into cells and path planning inside the cells. The area is decomposed using sweeping technique. For path planning inside the cells, a novel method is developed where optimal number of lanes are generated to minimize the number of UAV turns to accomplish the mission in minimum time. Also to allow operator interaction and precedence in coverage, a novel lawnmower/Zamboni planner is designed. Simulation results are presented for multiple UAVs are presented and flight test results for one UAV performing a persistent mission is presented.

A hierarchical control architecture for mobile offshore bases

Abstract A hierarchical architecture for mobile offshore bases (MOB) control is presented. By a control architecture we mean a specific way of organizing the motion control and navigation functions performed by the MOB. It is convenient to organize the functions into hierarchical layers. This way, a complex design problem is partitioned into a number of more manageable subproblems that are addressed in separate layers. The decomposition also allows for modular design and testing and the incorporation of plug-and-play components. This paper discusses the MOB requirements and maps them onto a layered control architecture. The formalization of the hierarchy is accomplished in terms of the specific functions performed by each layer and of the interfaces between layers. The implementation of the layers is discussed and illustrative examples are provided.

Coordinating UAVs and AUVs for oceanographic field experiments: Challenges and lessons learned

Obtaining synoptic observations of dynamic ocean phenomena such as fronts, eddies, oxygen minimum zones and blooms has been challenging primarily due to the large spatial scales involved. Traditional methods of observation with manned ships are expensive and, unless the vessel can survey at high-speed, unrealistic. Autonomous underwater vehicles (AUVs) are robotic platforms that have been making steady gains in sampling capabilities and impacting oceanographic observations especially in coastal areas. However, their reach is still limited by operating constraints related to their energy sources. Unmanned aerial vehicles (UAVs) recently introduced in coastal and polar oceanographic experiments have added to the mix in observation strategy and methods. They offer a tantalizing opportunity to bridge such scales in operational oceanography by coordinating with AUVs in the water-column to get in-situ measurements. In this paper, we articulate the principal challenges in operating UAVs with AUVs making synoptic observations for such targeted water-column sampling. We do so in the context of autonomous control and operation for networked robotics and describe novel experiments while articulating the key challenges and lessons learned.

Cluster-based communication topology selection and UAV path planning in wireless sensor networks

In conventional wireless sensor networks (WSNs) to conserve energy Low Energy Adaptive Clustering Heirarchy (LEACH) is commonly used. Energy conversation is far more challenging for large scale deployments. This paper deals with selection of sensor network communication topology and the use of Unmanned Aerial Vehicle (UAV) for data gathering. Particle Swarm Optimzation (PSO) is proposed as an optimization method to find the optimal clusters in order to reduce the energy consumption, bit error rate (BER), and UAV travel time. PSO is compared to LEACH using a simulation case and the results show that PSO outperforms LEACH in terms of energy consumption and BER while the UAV travel time is similar. The numerical results further illustrate that the performance gap between them increases with the number of cluster head nodes. The reduced energy consumption means that the network life time can be significantly extended and the lower BER will increase the amount of data received from the entrie network.